The present invention relates generally to a method for characterizing, classifying, and identifying unknown chemicals. Specifically, the present invention is a method for taking the data generated from an array of responses from a multichannel instrument, and determining the characteristics of a chemical in the sample without the necessity of calibrating or training the instrument with known samples containing the same chemical. The characteristics determined by the method are then used to classify and identify the chemical in the sample. The method can also be used to quantify the concentration of the chemical in the sample.
The characterization and identification of unknown chemical is a common requirement throughout an enormous variety of scientific inquiry, running across disciplines as diverse as biochemistry and environmental science. Unsurprisingly, there exist an equally enormous variety of techniques for determining the characteristics and identity of a chemical in a sample. Liquid and gas chromatography, mass spectroscopy, absorption spectroscopy, emission spectroscopy, and chemical sensors are but a few of the myriad of techniques scientists have devised in their efforts to characterize, classify, and identify unknown chemicals in samples.
Typically, these methods rely on inferences drawn from the information that is the output of a particular instrument. For example, methods that identify chemicals through absorption spectroscopy rely on the absorption of light at certain wavelengths when the sample containing the chemical is exposed to a light. By understanding the properties of a given chemical which give rise to absorption at certain wavelengths, scientists are able to infer some of a sample""s characteristics and perhaps identity the chemical(s) in the sample for example, by comparing the absorption spectra of a sample with a library of spectra taken from known chemicals. As such, these techniques often rely on determining the output signals of an instrument in response to chemicals whose identity and characteristics are known. Additionally, samples of chemicals whose concentrations are unknown may present problems for characterizing, classifying, identifying or quantifying unknowns using these types of instruments. Quantification often relies on rigorous calibration of the instrument in response to known samples of the chemical to be determined in the unknown samples. To overcome these and other difficulties, scientists have developed methods wherein a sample with an unknown chemical is interrogated with an array of channels from a particular instrument, for example, wherein the differences in the interactions between the various channels across the array with different chemicals is known from prior training and calibration on samples containing the same chemical as the unknown sample.
For example, a great many studies have described the use of arrays of chemical sensors to classify, identify, and quantify chemicals in a sample. Typically in these methods, the sensor array must be trained on samples containing chemicals of known identity and concentration in order to develop pattern recognition algorithms and calibration models that are used to classify, identify and quantify chemicals in unknown samples.[B. M. Wise, N. B. Gallagher, and M. W. A. U. S. A. Eigenvector Research, The process chemometrics approach to process monitoring and fault detection, J. Process Control, 6 (1996) 329-348. K. R. Beebe, R. J. Pell, and M. B. Seasholtz, Chemometrics: A Practical Guide, John Wiley and Sons, Inc., New York, 1998.] The only chemicals that can be classified, identified and quantified by this technique are chemicals to which the array has been previously exposed to generate output data that have been incorporated into the development of the pattern recognition algorithms and calibration models.
For example, acoustic wave sensors coated with layers of sorbent materials, such as polymers, have been investigated as array detectors by many groups.[J. W. Grate, S. J. Martin, and R. M. White, Acoustic Wave Microsensors, Part I, Anal Chem., 65 (1993) 940A-948A. J. W. Grate, S. J. Martin, and R. M. White, Acoustic Wave Microsensors, Part II, Anal. Chem., 65 (1993) 987A-996A. J. W. Grate, and G. C. Frye, xe2x80x9cAcoustic Wave Sensors,xe2x80x9d in Sensors Update, VSH, Weinheim, 1996, pp. 37-83.] Polymer-coated acoustic wave sensors are well understood in terms of the sensors"" transduction mechanisms and the interactions of analyte species with the polymeric sensing layers. A great variety of acoustic wave devices have been developed and demonstrated for chemical sensing applications in the gas and liquid phases. These include thickness shear mode (TSM) devices (also known as the quartz crystal microbalance or QCM), surface acoustic wave (SAW) devices, Leaky SAW devices, surface transverse wave (STW) devices, Love wave devices, shear-horizontal acoustic plate mode (SH-APM) devices, flexural plate wave (FPW) devices, thin film resonators, and thin rod flexural devices. Acoustic wave vapor sensors respond to any vapor that is sorbed at the sensing surface with a response that is proportional to the amount of vapor sorbed. The transduction mechanism of these sensors, which always involves a mass-loading contribution and often involves a polymer modulus change contribution, does not discriminate among sorbed species. Discrimination is dependent largely on the extent to which the applied polymer layer interacts with and sorbs particular chemical species. In addition, other sensor devices exist that are also sensitive to added mass, such as microbar, microbeam, and microcantilever devices.
The interactions between vapor molecules and polymeric sorbent phases are solubility interactions, which have been modeled and systematically investigated using linear solvation energy relationships (LSERs).[J. W. Grate, M. H. Abraham, and R. A. McGill, xe2x80x9cSorbent Polymer Coatings for Chemical Sensors and Arrays,xe2x80x9d in Handbook of Biosensors: Medicine, Food, and the Environment, CRC Press, Boca Raton, Fla., USA, 1996, pp. 593-612.]
In this approach, vapor solubility properties are characterized and quantified by solvation parameters related to polarizability, dipolarity, hydrogen bond acidity, hydrogen bond basicity, and dispersion interactions. The solvation parameters are the descriptors for vapor characteristics. LSER equations correlate the log of the partition coefficient of a vapor in a polymer with the vapor solvation parameters using a series of LSER coefficients related to the polymer solubility properties
LSERs are linear multivariate correlations with solvation parameters that have been applied to many systems, including water/air partition coefficients, the sorption of vapors by blood and tissue, toxicity of gases and vapors, adsorption on solid sorbents, adsorption on fullerene, and partitioning into gas-liquid chromatographic stationary phases. In addition, LSERs have been used to correlate various sensory measures with solvation parameters, including retention across frog olfactory mucosa, respiratory tract irritation, potency, nasal pungency thresholds and odor thresholds. The partitioning of vapors into sorbent polymers at 298K has been investigated with LSERs (correlation coefficients were typically 0.99), and these LSER equations have been used to estimate the responses of polymer-coated surface acoustic wave (SAW) vapor sensors. In addition, LSERs have been developed that correlate the responses of polymer-coated SAW devices to vapor solvation parameters. These yield LSER coefficients related to partitioning and detection of vapors with polymer films on SAW device surfaces.
When a polymer-coated acoustic wave vapor sensor is exposed to a vapor, the equilibrium distribution of the vapor between the gas phase and a polymeric sorbent phase on the sensor surface is given by the partition coefficient, K. This partition coefficient is the ratio of the concentration of the vapor in the sorbent polymer, CS to the concentration of the vapor in the gas phase, CV as shown in eq. 1.
K=CS/CVxe2x80x83xe2x80x83(1)
The response of a mass-sensitive acoustic wave sensor to absorption of a vapor into the polymeric sensing layer is related to the partition coefficient as shown in eq 2.
xcex94fV=nxcex94fSCVK/xcfx81xe2x80x83xe2x80x83(2)
The sensor""s response to the mass of vapor absorbed, a frequency shift denoted by xcex94fV, is dependent on the frequency shift due to the deposition of the film material onto the bare sensor (a measure of the amount of polymer on the sensor surface), xcex94fS, the vapor concentration, the partition coefficient, and the density of the sorbent phase, xcfx81. If the observed response is entirely due to mass-loading, n=1. If a modulus decrease of the polymer due to vapor sorption also contributes to the frequency shift, n can be some number greater than 1, with values from 2 to 4 suggested for certain polymers. Whatever the value of n, the observed response is proportional to the amount of vapor sorbed as expressed by the partition coefficient.
The LSER method for understanding and predicting polymer/gas partition coefficients is based on eq 3, which expresses log K as a linear combination of terms that represent particular interactions.                               log          ⁢                      xe2x80x83                    ⁢          K                =                  c          +                      rR            2                    +                      s            ⁢                          xe2x80x83                        ⁢                          π              2              H                                +                      a            ⁢                          xe2x80x83                        ⁢                          ∑                              α                2                H                                              +                      b            ⁢                          ∑                              β                2                H                                              +                      1            ⁢                          xe2x80x83                        ⁢            log            ⁢                          xe2x80x83                        ⁢                          L              16                                                          (        3        )            
In this relationship,       R    2    ,      π    2    H    ,      ∑          α      2      H        ,      ∑          β      2      H        ,
and log L16 are solvation parameters that characterize the solubility properties of the vapor, where R2 is a calculated excess molar refraction parameter that provides a quantitative indication of polarizable n and p electrons;   π  2  H
measures the ability of a molecule to stabilize a neighboring charge or dipole;   ∑      α    2    H  
and   ∑      β    2    H  
measure effective hydrogen-bond acidity and basicity, respectively; and log L16 is the liquid/gas partition coefficient of the solute on hexadecane at 298K (determined by gas-liquid chromatography). The log L16 parameter is a combined measure of exoergic dispersion interactions that increase log L16 and the endoergic cost of creating a cavity in hexadecane leading to a decrease in log L16. Henceforth, the parameters that describe characteristics of the sample more generally shall be referred to as xe2x80x9cdescriptors.xe2x80x9d Thus, in the case of polymer acoustic wave vapor sensors whose responses are modeled with LSERs, the descriptors are the solvation parameters       R    2    ,      π    2    H    ,      ∑          α      2      H        ,      ∑          β      2      H        ,
and log L16. Solvation parameters have been tabulated for some 2000 compounds
The LSER equation for a particular polymer is determined by regressing measured partition coefficients for a diverse set of vapors on that polymer against the solvation parameters of the test vapors. The regression method yields the coefficients (s, r, a, b, and l) and the constant (c) in eq 3. These coefficients are related to the properties of the sorbent polymer that are complementary to the vapor properties. The necessary partition coefficients for the determination of the LSER are generally obtained by gas chromatographic measurements, but they could also be determined from the responses of a mass-sensitive acoustic wave device with a thin film of the polymer. LSER equations derived from chromatographic measurements at 298 K have been reported for fourteen sorbent polymers suitable for use on acoustic wave devices. The polymer LSER coefficients will be referred to as polymer parameters. More generally, because the polymer is the portion of this multichannel instrument that directly interacts with the chemical to produce a measured response, the term xe2x80x9cinteractive parametersxe2x80x9d is inclusive of xe2x80x9cpolymer parametersxe2x80x9d.
In the past, sorption data for a vapor on multiple gas chromatographic stationary phases has been used in combination with xe2x80x9cpolymer parametersxe2x80x9d describing the stationary phases to obtain values for vapor solubility parameters to be assigned to known vapors. [M. H. Abraham, G. S. Whiting, R. M. Doherty, and W. J. Shuely, Hydrogen bonding. XVI. A new solute solvation parameter, pi2H, from gas chromatographic data, J. Chromatogr., 587 (1991) 213-228. F. Patte, M. Etcheto, and P. Laffort, Solubility Factors for 240 Solutes and 207 Stationary Phases in Gas-liquid Chromatography, Anal. Chem., 54 (1982) 2239-2247.] The method was not used to characterize or identify unknowns, nor was a method developed to characterize an unknown at unknown concentration developed.
Despite these advances, the prevailing paradigm in the use of multichannel analytical instruments for classification and identification of components of samples is that the array must be trained to recognize the component or components of interest. In this essentially empirical approach, components that were not in the training set cannot be classified or identified. Similarly, the paradigm for using sensor arrays for vapor classification and identification is that the array must be trained to recognize the vapor or vapors of interest. In this essentially empirical approach, chemicals that were not in the training set cannot be classified or identified. For example, if a sensor array instrument is trained and calibrated on samples containing known chemicals, and then is taken to the field to detect and identify chemicals, it will only be able to identify chemicals that were in the training. If it detects a chemical that was not in the training, that chemical will either be reported as detected but unknown, or it will be misidentified as being one of the chemicals in the training. Additionally, a general purpose instrument intended to classify or identify many chemicals would have to be trained on all those chemicals, and would not be able to classify or identify other chemicals. Thus there exists a need for a method for using the data from multichannel instruments which is capable of characterizing the properties of unknown chemicals without the necessity of training the multichannel instrument on those unknown chemicals. Similarly, there exists a need to be able to transform array responses into descriptors of the chemical properties which may then be used to classify and/or identify unknown chemicals. There also exists a need for a method which allows the characterization and classification of an unknown chemical even if the concentration is unknown, and the quantification of the concentration of an unknown chemical. Finally, there exists a need for a method which allows a multichannel instrument to be trained on a finite set of chemicals and then be able to apply the instrument to characterization, classification, identification, and/or quantification of additional chemicals.
Accordingly, it is an object of the present invention to provide a method for characterizing an unknown sample by obtaining a plurality of responses from a multichannel instrument, where the plurality of responses equal to or greater a plurality of descriptors, the plurality of responses is related to each of the plurality of descriptors, and the plurality of descriptors is determined from the plurality of responses.
It is a further object of the present invention to select the plurality of descriptors from the group comprising molecular interaction characteristics of the unknown sample, molecular properties of the unknown sample, molecular structural features of the sample, or combinations thereof.
It is a further object of the present invention to select the plurality of descriptors which are related to the solubility properties of the samples.
It is a further object of the present invention to select the plurality of descriptors as vapor solvation parameters.
It is a further object of the present invention to select the plurality of descriptors as parameters in a linear free energy relationship.
It is a further object of the present invention to select the plurality of descriptors as parameters in a linear salvation energy relationship.
It is a further object of the present invention to select the plurality of descriptors as descriptors in a quantitative structure activity relationship.
It is a further object of the present invention to select the plurality of descriptors as parameters in a principle components equation.
It is a further object of the present invention to model the response of each channel of a multichannel instrument with an equation including a term that is related to the plurality of descriptors.
It is a further object of the present invention to utilize a response of a multichannel instrument which is related to the thermodynamic partitioning of the unknown sample between phases.
It is a further object of the present invention to utilize a response of a multichannel instrument which is related to the partitioning of the unknown sample between the ambient environment and a plurality of sorbent phases.
It is a further object of the present invention to utilize a multichannel instrument which utilizes a plurality of gas chromatographic columns.
It is a further object of the present invention to utilize a multichannel instrument which utilizes a plurality of sensors having sorbent phases.
It is a further object of the present invention to utilize a multichannel instrument which utilizes a plurality of sensors having sorbent phases selected from the group comprising a solid surface, a self assembled monolayer, a molecular multilayer, an amorphous solid phase, a liquid, a membrane and a thin film.
It is a further object of the present invention to utilize a multichannel instrument which utilizes a stationary sorbent phase. It is a further object of the present invention to utilize a multichannel instrument which utilizes a sorbent phase as a polymer.
It is a further object of the present invention to utilize a multichannel instrument which utilizes a plurality of acoustic wave sensors selected from thickness shear mode devices, surface acoustic wave devices, Leaky surface acoustic wave devices, surface transverse wave devices, Love wave devices, shear-horizontal acoustic plate mode devices, flexural plate wave devices, thin film resonators, and thin rod flexural devices.
It is a further object of the present invention to utilize a multichannel instrument which utilizes a plurality of acoustic wave sensors coated with polymers and stationary phases.
It is a further object of the present invention to utilize a multichannel instrument which utilizes a plurality of optical sensors.
It is a further object of the present invention to utilize a multichannel instrument which utilizes a plurality of chemiresistor sensors.
It is a further object of the present invention to utilize a multichannel instrument which utilizes a plurality of chemiresitor sensors having a sorbent layer phase and a solid electronic conductor.
It is a further object of the present invention to utilize a multichannel instrument which utilizes a plurality of electrochemical or field effect transistor sensors.
It is a further object of the present invention to utilize a multichannel instrument which utilizes plurality of sensors selected from microbeam, microbar or microcantilever sensors.
It is a further object of the present invention to characterize an unknown sample, wherein the sample is modeled with a plurality of descriptors, by first obtaining a plurality of responses from a multichannel instrument, the plurality of responses equal to or greater than the plurality of descriptors, wherein the response from each channel of the multichannel instrument includes a term related to the plurality of descriptors and the term related to the plurality of descriptors contains coefficients for each descriptor; and determining the plurality of descriptors from the plurality of responses.
It is a further object of the present invention to utilize a multichannel instrument which utilizes coefficients determined from instrument responses to known compounds.
It is a further object of the present invention to utilize a multichannel instrument which utilizes coefficients determined from instrument responses to known compounds to characterize an unknown sample, wherein the sample is modeled with a plurality of descriptors, by obtaining a plurality of responses from a multichannel instrument, the plurality of responses equal to or greater than the plurality of descriptors, wherein the response from each channel of the multichannel instrument includes a term related to the plurality of descriptors, wherein the term related to the plurality of descriptors contains coefficients for each descriptor, defining a matrix P containing the coefficients, determining the plurality of descriptors from the plurality of responses and the matrix P.
It is a further object of the present invention to utilize a multichannel instrument which utilizes coefficients determined from instrument responses to known compounds to characterize an unknown sample, wherein the sample is modeled with a plurality of descriptors by obtaining a plurality of responses from a multichannel instrument, the plurality of responses equal to or greater than the plurality of descriptors, wherein the response from each channel of the multichannel instrument is included in matrix R where R is equal to C 10(VP+1c)Mxe2x88x921N, the descriptors are determined from matrix V, where V is related to a term of the form {log(Cxe2x88x921R M Nxe2x88x921)xe2x88x921c} PT(PPT)xe2x88x921; C is a diagonal matrix of the concentrations of the vapors (number of vapors by number of vapors), M and N are diagonal matrices (number of channels by number of channels) of particular properties of specific channels of the detector, N (number of sensors by number of sensors, or number of polymers by number of polymers) is a diagonal matrix of the xcex94fS values of the sensors, c is a vector of constants, PT is the transpose of matrix P, PT(PPT)xe2x88x921 is the pseudo-inverse of P, by defining a matrix P containing the coefficients and determining the plurality of descriptors from the plurality of responses and the matrix P.
It is a further object of the present invention to utilize a multichannel instrument which utilizes coefficients determined from instrument responses to known compounds to characterize an unknown sample, wherein the sample is modeled with a plurality of descriptors, by obtaining a plurality of responses from a multichannel instrument, the plurality of responses equal to or greater than the plurality of descriptors, wherein the response from each channel of the multichannel instrument is included in matrix R where R is equal to C 10(VP+1c) Dxe2x88x921F, the descriptors are determined from matrix V, where V is equal to {log (Cxe2x88x921R D Fxe2x88x921)xe2x88x921c} PT(PPT)xe2x88x921; where C is a diagonal matrix of the concentrations of the vapors (number of vapors by number of vapors), D is a diagonal matrix of the polymer densities (number of polymers by number of polymers), F is a diagonal matrix of the xcex94fS values of the sensors (number of sensors by number of sensors, or number of polymers by number of polymers), c is a vector of constants, PT is the transpose of matrix P, PT(PPT)xe2x88x921 is the pseudo-inverse of P, by defining a matrix P containing the coefficients, and determining the plurality of descriptors from the plurality of responses and the matrix P.
It is a further object of the present invention to utilize a multichannel instrument which utilizes coefficients determined from instrument responses to known compounds to characterize an unknown sample, wherein the sample is modeled with a plurality of descriptors, by obtaining a plurality of responses from a multichannel instrument, the plurality of responses equal to or greater than the plurality of descriptors, wherein the response from each channel of the multichannel instrument is included in matrix R where R is equal to C 10(VP+1c) Dxe2x88x921 F, the descriptors are determined from matrix V, where V is equal to {log (Cxe2x88x921 R D Fxe2x88x921)xe2x88x921c} PT(PPT)xe2x88x921; where C is a diagonal matrix of the concentrations of the vapors (number of vapors by number of vapors), D is a diagonal matrix of the polymer densities (number of polymers by number of polymers), F is a diagonal matrix of the xcex94fS values of the sensors (number of sensors by number of sensors, or number of polymers by number of polymers), c is a vector of constants, PT is the transpose of matrix P, PT(PPT)xe2x88x921 is the pseudo-inverse of P, by defining a matrix P containing LSER coefficients determined from measurements of thermodynamic partitioning, and determining the plurality of descriptors from the plurality of responses and the matrix P.
It is a further object of the present invention to utilize a multichannel instrument which utilizes coefficients determined from instrument responses to known compounds to characterize an unknown sample, wherein the sample is modeled with a plurality of descriptors, by obtaining a plurality of responses from a multichannel instrument, the plurality of responses equal to or greater than the plurality of descriptors, wherein the response from each channel of the multichannel instrument is included in matrix R where R is modeled as a function of C, SV, Vxe2x80x2, Pxe2x80x2, and SP, where, SV contains any sample specific parameters that influence the response independent of the specific interactions of the sample with each channel, Vxe2x80x2 contains said plurality of sample parameters, Pxe2x80x2 contains parameters specific to the properties of detector channels, SP contains channel specific sensitivity parameters, and C contains sample concentration information.
It is a further object of the present invention to utilize a multichannel instrument which utilizes coefficients determined from instrument responses to known compounds to characterize an unknown sample, wherein the sample is modeled with a plurality of descriptors, by obtaining a plurality of responses from a multichannel instrument, the plurality of responses equal to or greater than the plurality of descriptors, wherein the response from each channel of the multichannel instrument is included in matrix R equal to SV C 10(Vxe2x80x2Pxe2x80x2) SP.
It is a further object of the present invention to utilize descriptors determined from matrix Vxe2x80x2a equal to {log ( R SPxe2x88x921)} Pxe2x80x2aT(Pxe2x80x2aPxe2x80x2aT)xe2x88x921 where Vxe2x80x2a and Pxe2x80x2a are Vxe2x80x2 and Pxe2x80x2 augmented to capture SV C.
It is a further object of the present invention to utilize one or more of the descriptors determined according to the method of the present invention to classify an unknown sample as belonging to a class of chemicals with certain properties.
It is a further object of the present invention to utilize one or more of the descriptors determined according to the method of the present invention to classify an unknown sample as belonging to a class of chemicals with certain structural features.
It is a further object of the present invention to utilize one or more of the descriptors determined according to the method of the present invention to compare the descriptors to a table of descriptors of known chemicals to determine the identity of the unknown sample.
It is a further object of the present invention to provide a method for characterizing an unknown sample at an unknown concentration, wherein the sample is modeled with a plurality of descriptors by obtaining a plurality of responses from a multichannel instrument, the plurality of responses equal to or greater than the plurality of descriptors, wherein the response from each channel of the multichannel instrument includes a term related to the plurality of descriptors, wherein the term related to the plurality of descriptors contains coefficients for each descriptor; defining a matrix Pa containing the coefficients and augmented by a vector of ones, determining the plurality of descriptors and concentration from the plurality of responses wherein the response is included in matrix R where R is equal to 10(VaPa+1c) Dxe2x88x921 F; the descriptors and concentration are determined from matrix Va, where Va is equal to {log( R D Fxe2x88x921)xe2x88x921c} PaT(PaPaT)xe2x88x921, Pa is defined as the matrix P augmented by a vector of ones as given in             P      a        =          [                                    P                                                1                              ]        ,
where P is a matrix containing the coefficients, C is a diagonal matrix of the concentrations of the vapors (number of vapors by number of vapors), D is a diagonal matrix of the polymer densities (number of polymers by number of polymers), the superscript of xe2x88x921 denotes the inverse of the matrix, F is a diagonal matrix of the xcex94fS values of the sensors (number of sensors by number of sensors, or number of polymers by number of polymers), PaT is the transpose of Pa, PaT(PaPaT)xe2x88x921 is the pseudoinverse of Pa.
It is a further object of the present invention to provide a method for characterizing an unknown sample at an unknown concentration, wherein matrix Pa contains LSER coefficients determined from measurements of thermodynamic partitioning.
It is a further object of the present invention to provide a method for characterizing an unknown sample at an unknown concentration, wherein matrix V contains solvation parameters for vapors.
It is a further object of the present invention to provide a method for characterizing an unknown sample at an unknown concentration, wherein matrix R contains reponses of acoustic wave vapor sensors with sorbent interactor layers.
It is a further object of the present invention to provide a method for characterizing an unknown sample at an unknown concentration, wherein matrix Pa contains LSER coefficients determined from measurements of responses of acoustic wave vapor sensors to known vapors.
It is a further object of the present invention to provide a method for characterizing an unknown sample at an unknown concentration, wherein matrix V contains solvation parameters for vapors.
It is a further object of the present invention to provide a method for characterizing an unknown sample at an unknown concentration, wherein matrix R contains responses of acoustic wave vapor sensors with sorbent interactor layers.
It is a further object of the present invention to provide a method for characterizing an unknown sample at an unknown concentration, utilizing one or more of the descriptors to classify the unknown sample as belonging to a class of chemicals with certain properties.
It is a further object of the present invention to provide a method for characterizing an unknown sample at an unknown concentration, wherein the descriptors are utilized to classify the unknown sample as belonging to a class of chemicals with certain structural features.
It is a further object of the present invention to provide a method for characterizing an unknown sample at an unknown concentration, wherein the descriptors are compared to a table of descriptors of known chemicals to determine the identity of the unknown sample.
It is a further object of the present invention to provide a method for characterizing an unknown sample at an unknown concentration, wherein the sample is modeled with a plurality of descriptors by obtaining a plurality of responses from a multichannel instrument, the plurality of responses equal to or greater than the plurality of descriptors, wherein the plurality of responses is related to each of the plurality of descriptors; and determining one or more of the plurality of descriptors from the plurality of responses using the method of inverse least squares, where an individual descriptor, y, is modeled as a weighted sum of responses according to y=Xb, where X is the measured response and b is a vector of weights, generally determined by regression b=X+y
It is a further object of the present invention to provide a method for characterizing an unknown sample at an unknown concentration, wherein the regression is selected from the methods including multiple linear regression, partial least squares, and principle components regression.
It is a further object of the present invention to provide a method for characterizing an unknown sample at an unknown concentration, wherein b, the vector of weights for determination of each descriptor, is determined by a regression using responses to known compounds.
It is a further object of the present invention to provide a method for characterizing an unknown sample at an unknown concentration, wherein b, the vector of weights for determination of each descriptor, is determined by a regression using responses to known compounds to determine descriptors from the instrument response to unknowns that were not among the known compounds.
Accordingly, the present invention is a method of characterizing a component of a sample, beginning with the step of analyzing the sample with a multivariate instrument wherein each channel of the multivariate instrument gives a response that is related to various descriptors of the component.
A preferred embodiment of the present invention utilizes an array of polymer coated acoustic wave sensors as the multichannel instrument for data gathering, and is described in detail to provide an example of the practice of the present invention. The key aspect of this approach is that polymer-coated sensor responses are related to the solubility interactions between the polymer and the vapor, and the vapors"" solubility properties are quantified using solvation parameters. Therefore, the response vector from a polymer-coated sensor array encodes information about vapor solubility properties, and it is therefore possible, through the method of the present invention, to transform the array data (or response vector) into vapor solvation parameters. These parameters characterize the vapor, and can be used to additionally classify or possibly identify vapors. In addition, through the method of the present invention, the array data can be transformed into vapor solvation parameters and vapor concentration simultaneously.
While the invention is described with polymer-coated acoustic wave vapor sensors as an example of the present invention, the present invention is applicable to, and broadly encompasses, the use of any such multichannel instrument as data gathering mechanisms. Thus, the present invention should be understood as a method for characterizing a component in a samples for which a xe2x80x9cspectrumxe2x80x9d or pattern has not been determined in advance from experimental calibrations using the multichannel instrument, regardless of which multichannel instrument is selected for the gathering of the data. Also, while the polymer-coated acoustic wave vapor sensors lend themselves to a detection method related to thermodynamic partitioning, the present invention more generally relates to the interpretation of data from any multivariate detector where the response of each channel of the detector can be modeled by a mathematical relationship (linear, non-linear or combinations thereof) correlating responses with sample descriptors. The present invention then allows descriptors of chemicals not in the training set of the particular instrument to be extracted from the instrument response. These descriptors characterize the chemical in the sample and can be used to further classify or identify the chemical.
For example, as will be apparent to one having skill in the art, there exist many other sorbent phases that are not polymers whose sorbent properties can be modeled with linear solvation energy relationships, and that could be used as sorbent phases on sensors. In addition, it is apparent that there exist other relationships and other descriptors that can be used to model sorption, partitioning, and other processes relevant to the response of a multivariate analytical instrument. It is also apparent that there exist other types of acoustic wave sensors, and types of chemical sensors other than acoustic wave sensors whose responses are dependent on the sorption of a compound onto or into a layer deposited on the surface of the sensor. For example, microbar, microbeam and microcantilever sensors also can detect the mass of a chemical sorbed into a layer. Other types of sensors that rely on partitioning of a compound into a sorbent phase include optical and chemiresistor sensors, and these sensors can be used in arrays with various sorbent layers. Another instrument that relies on sorption into multiple phases is a multicolumn gas chromatograph. Membrane inlet mass spectrometers also involve sorption of vapors into a polymeric material as a part of the process of obtaining an analytical signal. As will be apparent to one having skill in the art, the method of the present invention is readily adaptable to all such sensor systems, and the present invention should be understood to contemplate and encompass the use of all such instruments and relationships.
As used herein, the term xe2x80x9cchemical(s)xe2x80x9d is inclusive of elements as identified on the periodic table of the elements, compounds that are combinations of those elements, and ions that are charged elements or compounds. As used herein, the term xe2x80x9ccharacteristic(s)xe2x80x9d means physical properties, chemical properties, molecular interactions, and structural features of the sample.
In one approach of the present invention, all the relevant parameters are solved for simultaneously. It is mathematically similar to a classical least squares solution in absorbance spectroscopy, where the observed response, R, is used to obtain the concentrations C given the analyte pure component responses S. However, in the present invention the observed response, R, is used to obtain numerical values of the descriptors.
A second preferred embodiment requires solving for each descriptor (vapor parameter in the case of polymer coated acoustic wave sensors) individually. This is the inverse least squares approach, where an individual descriptor, y, is modeled as a weighted sum of the responses.
One advantage of the present invention is that it is not necessary to know the concentration of the unknown chemical in the sample independently in order to solve for the characteristics of the unknown chemical in the sample. Thus, in the preferred embodiment of the present invention utilizing polymer coated acoustic wave sensors, it is not necessary to know the vapor concentration independently in order to solve for the vapor solvation parameters. Instead, the solvation parameters and log of the concentration of an unknown vapor can be solved for simultaneously using the responses of an array of characterized sensors.
The vapor parameters that characterize a chemical in a sample can be further used to classify the chemical in the sample. For example, a vapor could be classified as a hydrogen-bond base on the basis of a postive   ∑      β    2    H  
value. Alternatively, the parameter values could be used to classify a vapor as belonging to a particular compound class defined by multiple characteristics, such as a vapor that is both a hydrogen-bond base and a hydrogen bond acid. Additionally, the parameter values could be used to classify a vapor as belonging to a particular compound class, such as aliphatic hydrocarbon, aromatic hydrocarbon, or aliphatic alcohol, to name just a few.
The vapor parameters can be further used to identify the unknown chemical by comparison with a tabulation of vapor parameters for known chemicals.
Thus, the present invention represents a fundamentally different way to characterize chemical in a sample and to use that characterization to classify and possibly to identify the chemical. Additionally, it offers a fundamentally new way to quantify the concentration of a chemical from multivariate data. Provided that the multichannel instrument gives responses (multi-variate data) that can be mathematically related to sample descriptors, a chemical can be characterized even if the multi-channel instrument has never been trained on that chemical. In addition, the unknown concentration of a chemical in a sample can be estimated even if its identity is unknown and no experimental calibrations on that sample have been performed.
The subject matter of the present invention is particularly pointed out and distinctly claimed in the concluding portion of this specification. However, both the organization and method of operation, together with further advantages and objects thereof, may best be understood by reference to the following description taken in connection with accompanying drawings wherein like reference characters refer to like elements.